Book Image

R Deep Learning Essentials. - Second Edition

By : Mark Hodnett, Joshua F. Wiley
Book Image

R Deep Learning Essentials. - Second Edition

By: Mark Hodnett, Joshua F. Wiley

Overview of this book

Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.
Table of Contents (13 chapters)

Image classification using the MXNet library

The MXNet package was introduced in Chapter 1, Getting Started with Deep Learning, so go back to that chapter for instructions on how to install the package if you have not already done so. We will demonstrate how to get almost 100% accuracy on a classification task for image data. We will use the MNIST dataset that we introduced in Chapter 2, Image Classification Using Convolutional Neural Networks. This dataset contains images of handwritten digits (0-9), and all images are of size 28 x 28. It is the Hello World! equivalent in deep learning. There’s a long-term competition on Kaggle that uses this dataset. The script Chapter5/explore.Rmd is an R markdown file that explores this dataset.

  1. First, we will check if the data has already been downloaded, and if it has not, we will download it. If the data is not available at this...